Abnormal motion detector and monitor

ABSTRACT

In an embodiment, a seizure monitor provides intelligent epileptic seizure detection, monitoring, and alerting for epilepsy patients or people with seizures. In an embodiment, the seizure monitor may be a wearable, non-intrusive, passive monitoring device that does not require any insertion or ingestion into the human body. In an embodiment, the seizure monitor may include several output options for outputting the accelerometer/gyro or other motion sensor data and video data, so that the data may be immediately validated and/or remotely viewed. The device alerts and communicates to the outside care givers via wirelessly or wired medium. The device may also support recording of accelerometer or other motion sensor data and video data, which can be reviewed later for further analysis and/or diagnosis. The device and invention is also used and applicable for other body motion disorders or detection and diagnostics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/317,676 (Docket #64-5), entitled “Abnormal Motion Detector andMonitor,” filed Oct. 25, 2011; which, in turn, is divisional of U.S.patent application Ser. No. 12/154,085 (Docket #64-1), entitled“Abnormal Motion Detector and Monitor,” filed May 19, 2008, by VaidhiNathan, et al., which claims priority benefit of U.S. Provisional PatentApplication No. 60/930,766 (53-5), and all of the above listedapplications are incorporated herein by reference, in their entirety.

FIELD

This specification is related to medical devices.

BACKGROUND

The subject matter discussed in the background section should not beassumed to be prior art merely as a result of its mention in thebackground section. Similarly, a problem mentioned in the backgroundsection or associated with the subject matter of the background sectionshould not be assumed to have been previously recognized in the priorart. The subject matter in the background section merely representsdifferent approaches, which in and of themselves may also be inventions.

Over two million people (about 1-3% of the population) suffer fromepileptic seizures in the United States. During a seizure the patient isunable to get help, talk, think, or act. In many cases it is veryimportant for doctors and caregivers to be able to detect seizures andgive the patient immediate help. There are some types of seizures, ifnot attended to, that can be fatal. Currently there are no home orpersonal seizure monitoring or detecting devices. There areElectroencephalography (EEG) machines, which measure electrical activityin the brain. However, EEGs are for hospital use and are large andexpensive. The EEGs may analyze brainwaves to detect the onset or theoccurrence of a seizure. EEGs require probes to be mounted on thepatients' scalp to sense, extract, and transmit data. The probes areuncomfortable, intrusive, and awkward—restricting patients' movementsand cause scarring. In addition, the graphs from the EEGs need to bereviewed and interpreted manually by trained personnel, such as nursesand medical assistants.

BRIEF DESCRIPTION

In the following drawings like reference numbers are used to refer tolike elements. Although the following figures depict various examples ofthe invention, the invention is not limited to the examples depicted inthe figures.

FIG. 1A shows a block diagram of an embodiment of seizure detectionsystem 100.

FIG. 1B shows an embodiment of a seizure detection device.

FIG. 2A shows a block diagram of a system, which may be incorporatedwithin the system of FIG. 1A.

FIG. 2B shows a block diagram of an embodiment of a memory system.

FIG. 3A shows a block diagram of an embodiment of a motion detector.

FIG. 3B shows a block diagram of a camera.

FIG. 4 is a flowchart of an embodiment of a method of detecting aseizure, based on optical flow.

FIG. 5 is a flowchart of an embodiment of a method of detecting aseizure, based on motion analysis.

FIG. 6 is a flowchart of an embodiment of a method of detecting aseizure, based on patterns of motion vector patterns.

FIG. 7 is a flowchart of an embodiment of a method of detecting seizuresby measuring motion, via accelerometers and/or gyro sensors.

FIG. 8 shows a graph of three orthogonal components of acceleration ofan arm.

FIG. 9 shows a graph of two parameters of motion.

FIG. 10 shows an embodiment of a seizure detection, analyzing, andmonitoring device.

DETAILED DESCRIPTION

Although various embodiments of the invention may have been motivated byvarious deficiencies with the prior art, which may be discussed oralluded to in one or more places in the specification, the embodimentsof the invention do not necessarily address any of these deficiencies.In other words, different embodiments of the invention may addressdifferent deficiencies that may be discussed in the specification. Someembodiments may only partially address some deficiencies or just onedeficiency that may be discussed in the specification, and someembodiments may not address any of these deficiencies.

In general, at the beginning of the discussion of each of FIGS. 1A-3B isa brief description of each element, which may have no more than thename of each of the elements in the one of FIGS. 1A-3B that is beingdiscussed. After the brief description of each element, each element isfurther discussed in numerical order. In general, each of FIGS. 1A-9 isdiscussed in numerical order and the elements within FIGS. 1A-9 are alsousually discussed in numerical order to facilitate easily locating thediscussion of a particular element. Nonetheless, there is no onelocation where all of the information of any element of FIGS. 1A-9 isnecessarily located. Unique information about any particular element orany other aspect of any of FIGS. 1A-9 may be found in, or implied by,any part of the specification.

In an embodiment, a seizure monitor provides intelligent epilepticseizure detection, monitoring, and alerting for epilepsy patients and/orother people that experience seizures. In an embodiment, the seizuremonitor is a small consumer usable device that is wearable and can besetup and used easily by patients. In an embodiment, the seizure monitoris compact and low cost. The seizure monitor may have at least any oneof the following three different configurations or embodiments using—(i)motion sensor data (such as data from accelerometers, gyroscope sensorsand/or other motion sensor data), or (ii) video data, and/or (iii)hybrid data (which may include both video and accelerometer or othermotion sensor data). In an embodiment, the seizure monitor may be awearable, non-intrusive, passive monitoring device that does not requireany insertion or ingestion into the human body. In an embodiment, theseizure monitor may include several flexible and easy output options foroutputting motion data, so that the data may be immediately validatedand/or remotely viewed. The seizure monitor may also support recordingof motion data that can be reviewed later by a medical professional forfurther analysis and/or diagnosis.

Seizure Detection System (FIGS. 1-3B)

FIG. 1A shows a block diagram of an embodiment of seizure detectionsystem 100. Seizure detection system 100 may include cameras 102 a-n,communications line 104, surfaces 106 a-m, patient 108, motion detector110, and receiver 112. In other embodiments, seizure detection system100 may include additional components and/or may not include all of thecomponents listed above.

Seizure detection system 100 may detect, monitor, and/or alert anconcerned party of the onset and occurrence of epileptic seizures inpatients. In this specification the term “concerned party” includes anyentity or person that may have an interest in knowing about theoccurrence of a seizure, such as caregiver, medical professional, closefriend, or relative of the patient. In an embodiment, seizure detectionsystem 100 may be passive, compact, and/or non-intrusive.

Although throughout the specification seizure detection system 100 isdiscussed, seizure detection is just one example of a motion disorderthat may be detected with seizure detection system 100. Although thediscussion of this specification focuses on seizure monitoring anddetection, there are other motion based diagnostics or body motionanalysis that may be performed using the same system.

Each analysis can be different. But the HW can use the same. Just therules or conditions or pattern recognition can be different.

Cameras 102 a-n may include any number of cameras, which film a patientin order to capture on film images that may be analyzed to determinewhether a seizure is in progress. As also elaborated upon elsewhere,cameras 102 a-n are optional. Cameras 102 a-n are optional, and may bereplaced with another form of detecting seizures, such as motiondetectors. For example, another type of motion detector, such asinfrared detectors, may be used instead of, or in addition to, cameras102 a-n. Although cameras 102 a-n are illustrated as being mounted onsurfaces within a premise of the patient, cameras 102 a-n may be mountedon the patient, and instead of observing the patient to determine themotion of the patient, the motion of patent may be inferred from theimages of the surroundings of the patient, for example.

Optical sensors, such as video camera 102 a-n can be used to monitor thepatient and detect seizure-like activities. Seizure-like activities mayoffer unique motion patterns and can be distinguished fromnon-seizure-like activities. Easily distinguishable feature points (orfeature points corresponding to particular body parts that aresignificant in determining whether or not a seizure is in progress) inthe scene can be computed (such as those on the person or an object) andthe temporal motion patterns of the feature points (such as points onthe person's body or clothes) can be analyzed across frames fordetection of any abnormal activity. Some motion patterns of interestcould be large movements in short periods of time, repetitive movements,back-and-forth movements, etc. Further, some special seizure activitiesoffer very characteristic and predictable motion patterns, a priorknowledge of which can be utilized effectively to detect such cases.

Communications line 104 communicatively connects cameras 102 a-n to aprocessor (for analyzing the motion data and determining whether aseizure is occurring) and/or seizure alert system. In an embodiment,instead of or in addition to communications line 104, cameras 102 a-nmay communicate wirelessly with a processor and/or seizure alert system.

Surfaces 106 a-m support cameras 102 a-n. Patient 108 suffers fromseizure and is monitored by the rest of seizure detection system 108 todetermine whether a seizure has occurred. In this specification the word“patient” refers to any individual that is being monitored to determinewhether a seizure is occurring.

Motion detector 110 is mounted on patient 108 so that motions of thepatient may be measured. In an embodiment, motion detector 110 is anaccelerometer, a gyro sensor, and/or a hybrid of both. Motion detector110 communicates wirelessly (or via wires) with a seizure alert system.Motion detector 110 may include a transmitter for transmittinginformation about motion measured by motion detector 110. Optionally,motion detector 110 may include a location determining unit (e.g., aglobal positioning unit) for determining the location of patient 108 andtransmitting the location of the patient 108 to a concerned party. In anembodiment there are multiple motion detectors 110 mounted on patient108. In another embodiment, there is only one motion detector 110mounted on patient 110. If motion detector 110 is a small accelerometer(and/or gyro sensor), no other element needs to be worn or placed onpatient 108, and the accelerometer (and/or gyro sensor) may be passiveand non-intrusive. Although often in this specification an accelerometerand/or gyro are referred to another motion sensor(s) may be usedinstead. The detection of the seizure or movement patterns can be doneinside the watch itself. The results and/or an alert may be sent outsidevia wireless or wired medium. Alternatively, the watch can simply sendonly the sensor data, and the detection and decision can be made outsideon the phone or a computer outside the watch sensor. Both options areviable.

One way of detecting seizures is to monitor the motion of one or moreparts of the body of patient 108. During a seizure there are rapid andjerky movements of one or more body parts, such as the hands, legs,torso, and head. Seizures can be detected by measuring the change inoutput of a motion detector, the frequency of the change, and/oramplitude of the change indicating a movement of one or more body parts.

There are different types of motion detectors, which may be used ofmotion detector 110. One type of motion detector is an accelerometer,which measures acceleration. Ordinarily, when stationary, each part ofthe body experiences the acceleration of gravity (an accelerometercannot tell the difference between a body being accelerated as a resultof the body's changes in velocity and the body being pulled by a force,such as gravity). From the changes in acceleration, changes in positionand/or velocity may be inferred. When a body part moves, theacceleration of the body part changes, and thus the change in theacceleration indicates a motion. Another type of sensor data is gyrosensor data. Gyro sensors may detect the rotation along X, Y, and/or Zaxes. The rotation angles and position can also be used to detect motionand particular types of motion. While accelerometer measures linear axischanges, gyros measure the rotation changes.

The sensor that is used as motion detector 110 may a small device thatcan fit into an enclosure the size of a wristwatch. The motion data maybe measured in two-dimensions (e.g., along two perpendicular axes, whichmay be referred to as the X and Y axes) and/or three-dimensions (e.g.,along three perpendicular axes, which may be referred to as the X, Y,and Z axes). Acceleration, frequency, and amplitude (angle, angularvelocity, angular acceleration, and angular impulse or jerk) valuesabove a certain threshold are indicative of abnormal body movements thatoccur during a seizure. If two two-dimensional (X,Y) motion sensors areused as motion detector 110, each of the two-dimensional (X,Y) motionsensors may be paced along perpendicular axes. The two-dimensionaland/or three-dimensional motion sensors can be useful to detect thejerky and back and forth movements. The detection algorithm is discussedbelow. Motion detector 110 can be positioned and/or mounted on thepatient's arms, legs, bedclothes, and/or the bed itself. Motion detector110 placed on the patient's body may be more effective and accurate indetecting seizures.

Seizure alert system 112 alerts a concerned party when a seizure occurs.Seizure alert system 112 may be a PC, laptop, PDA, mobile phone bell,and/or other unit capable of indicating an alert. Information in signalsfrom cameras 102 a-n and/or motion detector 110 are analyzed, and if itis determined that a seizure is occurring, an alert is output from alertsystem 112. Seizure alert system 112 may include a monitor fordisplaying seizure alerts and/or for displaying motion data. In anembodiment, seizure alert system 112 may include a processor foranalyzing the signals from cameras 102 a-n and/or motion detector 110.In an embodiment, system 100 is a general purpose alerting system andcan also alert other motion disorders.

Motion sensors that are included within motion detector 110 may beattached to the wrist explicitly capture the motion along the x, y, andz directions. The data obtained may be a time sequence of theinstantaneous acceleration experienced by the sensor. Several approachescan be used here to detect any abnormal seizure-like activities. Theseapproaches can be divided broadly into two categories:

In an embodiment, a processor, which may be located in seizure alertsystem 112 or elsewhere may run algorithms to determine whether aseizure is occurring, and when it is determined that a seizure isoccurring alerts may be output from seizure alert system 112. In anembodiment, along with the alerts, one or more confirmation imagesand/or accelerometer or other motion sensor plots may also be sent to aconcerned party, such as a doctor and/or other caregiver. The alert maybe sent, via SMS, MMS, email, IM, or WAP or other message protocol. Thealert may include an alert message, alarm signal, beeps, local in-devicesound alerts, and/or alarms, which may be produced by a PDA, mobilephone, or other device, which may have built in alarms and alerts.

Receiver 114 receives signals from transmitters on wireless units suchas motion detectors 110. Receiver 114 is optional. If cameras 102 a-n donot communicate via communications line 104 (e.g., if communicationsline 104, is not present), receiver 114 may receive signals from cameras102 a-n. Thus, depending on the embodiment, seizure detection system 100may detect motion via video data from cameras 102 a-n, sensor data frommotion detector 110, and/or hybrid data (which is data based on bothcameras 102 a-n and/or sensor data from motion 110).

One example of an embodiment encompassed within FIG. 1A may includeinput sensors (video and/or motion sensors, such as accelerometers), oneor more computers for receiving and processing data from the sensors,connectivity interfaces, and a system for remotely monitoring and foralerting (e.g., sending an alarm) a concerned party. The connectivityinterface may include any of a number of communications interfaces, suchas Bluetooth interfaces and/or Wifi, wireless interfaces, and/or wiredinterfaces, which may use IP/LAN connections and/or serial portconnections, such as USB.

Another example of an embodiment encompassed within FIG. 1A may be avideo based system, which may include one or more cameras (such asanalog cameras, WebCam cameras, and/or IP/Network cameras) and/or IRdetectors and/or illuminators for nighttime monitoring and analysis. Thecameras may be color or infrared cameras, for example. The cameras canbe connected to the a Personal Computer (PC) and/or any computingdevice, via (i) a wireless interface, such as a WIFI/LAN interface, aSerial/USB wireless interface, and/or BlueTooth interface, and/or (ii) awired interface such as a LAN and/or Ethernet interface. Video data isreceived by the computing device analyzed and/or processed. The resultsof the analysis and/or processing are transmitted to the concernedparty. Alternatively, processing intelligence, an analysis engine,and/or algorithms that reside in the computing device may be embeddedand/or otherwise built into the camera resulting in a “Smart Camera.” Inan embodiment, there may be a camera and optional IR illuminators fornighttime monitoring and analysis, and there may not be anaccelerometer. There may be a processor that executes the algorithm, andprocessor may be located inside or outside of the camera. Detection as aresult of analyzing the motion data may occur within the processor inthe camera or in a processor located elsewhere. Alerts may becommunicated via an external media to a concerned party.

In a video based embodiment, the input data to the processor may be astream of video data from the camera. The camera may be connected to aPC or other computing device, such as a PDA or smart mobile phone. Thecamera may also have an intelligent processor embedded inside, thatprocesses and analyzes the input data.

In a film based seizure detection system, alerts and other output maycommunicate via alarms, SMS, MMS, IM, WAP message, email, or a phonecall to the concerned part. Alerts may be sent either over a LAN (awired network), a wireless network, or over a GSM/cellular mobilenetwork.

In an embodiment, the motion data from cameras 102 a-n and/or frommotion detector 110 is transformed into the frequency domain, via aFourier transform, or wavelet transform. In the frequency domain, themotion data is analyzed to see whether the motion data includes uniquefeatures in the frequency domain (such as larger coefficientscorresponding to high frequency components) that are expected to befound in motion data from a seizure. Performing a Fourier transform on aset of data taken within a particular window of time may be taken,(which may be referred to as a “windowed Fourier transform” and) whichmay capture the local nature of the signal. Similarly, orthogonalwavelet transforms (such as Daubechies) or another transform of themotion data may be taken (which may also provide a local representationof the signal in terms of the set of basis functions). The transformedmay then be scanned for large coefficients of basis vectors that areexpected to be associated with a seizure. The recognition seizure motionpatterns in the frequency domain may be performed based on a supervisedor unsupervised learning.

In an embodiment, there may be a motion sensor (an accelerometer or gyrosensor) without any video camera, which is simpler and cheaper. Theremay be a processor that operates the algorithm, and processor may belocated inside or outside of the motion sensor. In other words,detection may be performed via an algorithm executing within theprocessor to determine if a particular motion is a seizure. Alerts maybe communicated from the processor via external any of a number of mediato a concerned party.

In the embodiment that is based on a motion sensor, the input data maybe a stream of analog or digital signals or values from theaccelerometer/gyro sensor. The accelerometer or other motion sensor mayhave a built in processor to interpret and process the input data.Alternatively the accelerometer or other motion sensor may be connectedto a PC or other computing device, such as a smart mobile phone or PDA.The processor may execute seizure detection algorithms.

In the embodiment, that in which an accelerometer is the motion detector110, alerts and other output may be communicated via alarms, SMS, WAPmessages, email, or phone calls to the people specified. Alerts may besent either over LAN (wired) or wirelessly or over the GSM/cellularmobile networks.

In an embodiment, both video camera and accelerometer/gyro sensor may beused to determine whether there is a seizure. This embodiment mayinclude features and elements of both video and accelerometer/gyrosensor systems, mentioned above. Both detectors may run in parallel.Images from the video system may be used for additional confirmation andvalidation of the data from the accelerometer systems. These systems mayrun 24 hours per day, seven days a week, 365 days per year, all thetime, or on an as-needed basis. The seizure detection system 100,seizure alert system 112, cameras 102 a-n, motion detector 11 may bepowered externally or may be powered by a battery. In an embodiment,there may be multiple levels or thresholds. For example there may be twolevels or thresholds each indicating a different degree of danger orindicating a different degree of certainty that a seizure occurred. Insome cases, there may be a threshold for even suspected conditions maybe recorded. In one embodiment, one threshold or level may indicate thatthere is a problem that is observed, which may record the event orcondition without actually activating an alert and at a second level orthreshold an alert may be activated. Activating the alert may includesending a communication indicating the alert. In an embodiment, alertsare sent for only conditions or seizures that pass certain conditions orlevels. In an embodiment, there may be a button to indicate that thereis a seizure occurring now, which may include an ask-for-help button. Inan embodiment, the patient may be able to manually trigger the alert tobe activated. In an embodiment, if the system detects a problem, but thepatient is fine, the patient can press a button and indicate to ignorethe alarm and that the call this false alarm. These thresholds can beadjusted by the patient/user. Each user may have different threshold orpersonal requirement on when to record and when to alert. The system mayprovide two adjustable sets of thresholds that can be modified. One setof thresholds is for sending an alert and one set of thresholds is forrecording events that have seizure-like characteristics, but areexpected not to be a seizure or for events that are near the borderlinebetween being a seizure and not being a seizure. The input parametersmay be entered manually by the user, stored, and reused, so that theuser not need to input the parameters every time system 100 is turned onor put in use.

In this approach, we utilize the input signal as is. The data can bewindowed (overlapping) over short durations of time and the range ofvalues examined. It is expected that non-seizure-like activities exhibitvalue fluctuations only within a short range of values of acceleration(or gyro sensor), that is, the difference between the minimumacceleration value and the maximum acceleration value over a shortperiod of time is bounded by two relatively close values. On the otherhand, seizure-like activities exhibit a larger fluctuation and thedifference between the minimum acceleration value and the maximumacceleration value.

There at least are four strategies to implement the detection system.Any one of these four strategies is sufficient and can be used to do thedetection (1) learning based and/or (2) rules, conditions and/or logicbased, (3) probabilistic/statistical models and detection methods and(4) analysis of local, regional, global features which include both dataand temporal information. A hybrid of any combination these fourstrategies can also be used. Seizure detection system 100 may be basedon learning system and incorporate supervised or unsupervised learning,which may include one or more neural-networks, machines that performpattern recognition methods and/or support vector machines, for example.In embodiments including unsupervised learning, positive and negativedata samples are provided to seizure detection system 100 as trainingexamples for classifying patterns of behavior as seizure or non-seizuremotion patterns. After being fed the training examples, seizuredetections system 100 is able to make a determination as to whetherother motion patterns are associated with seizures. By usingunsupervised learning, after a training session and/or after learningfrom experience with actual motion patterns of patient 108, seizuredetection system 100 is able to detect seizures having motion patternsthat do not have features that are otherwise common amongst most otherseizure-like activities. For example, a neural-network or a SupportVector Machine can be trained based on positive and negative datasamples.

Alternatively, the detection system or algorithm can be based on rules,conditions, or equations and pre-defined logic that is patientindependent. For example, the rule or logic can be to compute the localpeaks of motion jerks. If there are N jerks happen within M seconds,then the motion may be determined to be seizure. For example, if thenumber of jerks are greater than 5 within 3 seconds, then it is aseizure. Typical rules/logic will use one or more conditions or criteriabased on: frequency/number of motion/jerks within a time frame,amplitude of the motion, continued change or duration of this motion,1^(st) or 2^(nd) degree change of these values above. Theserules/logic/conditions will change between the type of seizures likeTonicClonic, Partial or Complex Seizures etc. Caregivers or patients canalso adjust these rules/conditions/thresholds, to better suit theirindividual needs and type of seizures. There can also be defined set oftypes of conditions or detection rules templates, built in and users canselect and chose and test different ones and pick the ones they likemost.

In an embodiment, the motion data form cameras 102 a-n and/or frommotion detector 110 is transformed into the frequency domain, via aFourier transform, or wavelet transform. In the frequency domain, themotion data is analyzed to see whether the motion data includes uniquefeatures in the frequency domain (such as larger coefficientscorresponding to high frequency components) that are expected to befound in motion data from a seizure. Performing a Fourier transform on aset of data taken within a particular window of time may be taken,(which may be referred to as a “widowed Fourier transform” and) whichmay capture the local nature of the signal. Similarly, orthogonalwavelet transforms (such as Daubechies) or another transform of themotion data may be taken (which may also provide a local representationof the signal in terms of the set of basis functions). The transformedmay then be scanned for large coefficients of basis vectors that areexpected to be associated with a seizure. The recognition seizure motionpatterns in the frequency domain may be performed based on a supervisedor unsupervised learning.

In an embodiment there may be a bank of motion detectors, which mayinclude any combination of cameras 102 a-n, motion detector 110, otherhybrid motion detectors, and/or other motion detectors, as describedabove, each running in parallel. The usage of a bank of motion detectorscreates a system that is very robust and that detects seizures with ahigh level of accuracy.

In an embodiment, an accelerometer in a watch measures the accelerationsin the three directions, a_(x), a_(y), and a_(z). Optionally, from theindividual components that magnitude can be computed from a=SQRT(a_(x)²+a_(y) ²+a_(z) ²).) Optionally, the magnitude may be used to computethe absolute first derivative of the acceleration v=|a_(n)−a_(n-1)|,which is equivalent to “jerk.” Optionally, the absolute secondderivative may be computed v′=|a_(n)−2a_(n-1)+a_(n-2)|. The first and/orsecond derivative of each component may be computed and/or the firstand/or second derivative of magnitude may be computed. In an embodiment,a count is performed of all of the peaks of the first and/or secondderivative that occur during a specified time window that are above acertain threshold. If the number of peaks is greater than a thresholdnumber, it is assumed that a seizure is occurring.

If a preset number of peaks in the amplitude of the acceleration areobtained within a certain time interval, then it is considered to be aseizure. If less than the preset number of peaks in the amplitude of theacceleration are obtained within the time interval, then it is assumedthat a seizure did not occur. In an embodiment, the variation of eachcomponent of acceleration is analyzed separately (in addition to orinstead of analyzing the magnitude of the acceleration). During aseizure, in each component of the acceleration, the peaks may be morefrequent and shorter than for the amplitude, and consequently, for eachcomponent of acceleration, the threshold for the number of peaks (thatis considered indicative of a seizure) in a given time period may be sethigher and the threshold for the amplitude of the peaks (that isconsidered indicative of a seizure) may be set lower than for themagnitude. The positive and negative examples of motion patterns thatare fed to a neural network or other learning algorithm may include eachcomponent of acceleration and/or the magnitude of the acceleration.

FIG. 1B shows a representation an embodiment of seizure detection system150. Seizure detection system 150 band 152, housing 154, display 156,and input interface 158. In other embodiments, seizure detection system150 may include additional components and/or may not include all of thecomponents listed above.

Seizure detection system 150 is an embodiment of a seizure detectionsystem that is a device that is also a wristwatch, that is within adevice that is a wristwatch, or doubles as a wrist watch. Otherembodiments of seizure detection system 150 may be worn elsewhere on anarm, on a hand, on a leg, on a foot, on a chest, and/or other part of aperson. Seizure detection system 150 may include a motion detector (notshown in FIG. 1B), such as an accelerometer, for motion detector 110(FIG. 1A). Band 152 may be used for fastening seizure detection system150 to a wrist of patient 108 (FIG. 1A). Housing 154 contains thecircuitry for seizure detection system 100 (FIG. 1A). Display 156 maydisplay settings of seizure detection system 150, the time, and/oroutput of seizure detection 156. Input interface 158 may be a series ofbuttons for inputting settings for seizure detection system 150 and/orfor inputting wristwatch settings.

FIG. 2A shows a block diagram of system 200, which may be incorporatedwithin the system of FIG. 1A. System 200 may include output system 202,input system 204, memory system 206, processor system 208,communications system 212, and input/output device 214. In otherembodiments, system 200 may include additional components and/or may notinclude all of the components listed above.

System 200 may be an embodiment of seizure detection system 100 in whichseizure detection system 200 is contained within one unit. Alternativelyor additionally, an embodiment of seizure detector 112 may be system200. Output system 202 may include any one of, some of, any combinationof, or all of a monitor system, a handheld display system, a printersystem, a speaker system, a connection or interface system to a soundsystem, an interface system to peripheral devices and/or a connectionand/or interface system to a computer system, intranet, and/or internet,for example. Output system 202 may include lights, such as a red lightand/or a flashing light to indicate a seizure. Output system may includesounds such as beeps, rings, buzzes, sirens, a voice message, and/orother noises. Output system 202 or a part of output system 202 may bekept in the possession of a care taker or in a location that will catcha care taker's attention, such as a PDA, cell phone, and/or a monitor ofa computer that is viewed by a care taker. Output system 202 may send ane-mail, make a phone call, and/or send other forms of messages to alerta concerned party about the occurrence of a seizure.

Input system 204 may include any one of, some of, any combination of, orall of a keyboard system, a mouse system, a track ball system, a trackpad system, buttons on a handheld system, a scanner system, a microphonesystem, a connection to a sound system, and/or a connection and/orinterface system to a computer system, intranet, and/or internet (e.g.,IrDA, USB), for example. Input system 204 may include a motion detectorand/or camera for detecting high frequency motion. Input system 204 or apart of input system 204 may be kept in the possession of a care takeror in a location easily accessible to a concerned party so that theconcerned party may request current motion information and/or pastmotion and/or seizure information. For example, input system 204 mayinclude an interface for receiving messages from a PDA or cell phone ormay include a PDA and/or cell phone.

Memory system 206 may include, for example, any one of, some of, anycombination of, or all of a long term storage system, such as a harddrive; a short term storage system, such as random access memory; aremovable storage system, such as a floppy drive or a removable drive;and/or flash memory. Memory system 206 may include one or moremachine-readable mediums that may store a variety of different types ofinformation. The term machine-readable medium is used to refer to anymedium capable carrying information that is readable by a machine. Oneexample of a machine-readable medium is a computer-readable medium.Another example of a machine-readable medium is paper having holes thatare detected that trigger different mechanical, electrical, and/or logicresponses. Memory system 206 may store seizure detection engine and/orinformation about seizures. Memory system 206 will be discussed furtherin conjunction with FIG. 2B. If system 200 is seizure alert system 112,memory system 206 is optional, because the processing and storage ofseizure information may occur elsewhere.

Processor system 208 may include any one of, some of, any combinationof, or all of multiple parallel processors, a single processor, a systemof processors having one or more central processors and/or one or morespecialized processors dedicated to specific tasks. Processor system 208may run a program stored on memory system 206 for detecting seizures,which may be referred to as a seizure detection engine. Processor system208 may implement the algorithm of seizure detection system 200.Processor system 208 may collect the data from one or moreaccelerometers and/or video sensors. Processor system 208 may implementa detection and analysis algorithm on the data. If system 200 is anembodiment of seizure alert system 112, processor system 208 isoptional, because the processor may be located elsewhere.

As a digression, if seizure detection system 112 is not one unit, theprocessor system may be located at one of at least four locations, whichinclude within an external device such as a PC or laptop, within ahandheld device, within a camera, within an accelerometer. Data may bestreamed to the external device via a wired connection (such asLAN/USB/Serial) and/or a wireless connection (such as Wifi/BT). Thehandheld computing device may be a PDA, mobile phone, or other handhelddevice. In other words, the detection engine and algorithm may resideinside the handheld device. The data may be streamed to the mobile phoneor hand-held/PDA, and the processing and/or analysis may be executed onthe handheld device. The processor of seizure detection system 100 maybe located and built into any one of or any combination of cameras 102a-n. In other words, the processor with the detection engine (thesoftware that analyzes the sensor data to determine whether a seizureoccurred) may be embedded inside of any one of or any combination ofcamera 102 a-n and the detection processing may be carried out insidethe camera. In an embodiment, processor system 208 may be located withina handheld device, which may be an embodiment of seizure alert system112 and/or seizure detection system 100 may be a handheld devicestrapped to patient 108 (FIG. 1A) in which processor 208 is located.

Communications system 212 communicatively links output system 202, inputsystem 204, memory system 206, processor system 208, and/or input/outputsystem 214 to each other. Communications system 212 may include any oneof, some of, any combination of, or all of electrical cables, fiberoptic cables, and/or means of sending signals through air or water (e.g.wireless communications), or the like. Some examples of means of sendingsignals through air and/or water include systems for transmittingelectromagnetic waves such as infrared and/or radio waves and/or systemsfor sending sound waves.

Input/output system 214 may include devices that have the dual functionas input and output devices. For example, input/output system 214 mayinclude one or more touch sensitive screens, which display an image andtherefore are an output device and accept input when the screens arepressed by a finger or stylus, for example. The touch sensitive screensmay be sensitive to heat and/or pressure. One or more of theinput/output devices may be sensitive to a voltage or current producedby a stylus, for example. Input/output system 214 is optional, and maybe used in addition to or in place of output system 202 and/or inputdevice 204.

FIG. 2B shows a block diagram of an embodiment of memory system 206.Memory system 206 may include seizure detection algorithm 242,characteristic seizure data 244, records on past seizures 246, anddevice drivers 248. In other embodiments, memory system 206 may includeadditional components and/or may not include all of the componentslisted above.

Seizure detection algorithm 242 analyzes motion data to determinewhether a seizure has occurred. Characteristic seizure data 244 includesinformation characterizing a seizure. Characteristic seizure data 244may include thresholds for various parameters that are indicative of aseizure having taken place. For example, characteristic seizure data mayinclude one or more thresholds for the frequency of oscillation of avarious body parts during a seizure, thresholds for frequency ofoscillation of the acceleration or other parameter output by theaccelerometer and/or a threshold of the frequency of oscillation ofcantilever that is part of the an accelerometer that is included withinmotion detector 110. Characteristic seizure data 244 may includepatterns of data that are indicative of a seizure. Characteristicseizure data 244 may include default data that is not specific to anyone individual and/or may include data that is specific to patient 108.

Records of past seizures 246 may store information about seizures as theseizures are happening, which may be reviewed further by at a later dateto better determine the characteristics of the seizures that arespecific to patient 108 so that seizure detection system 100 may morereliably detect the seizures of patient 108. Additionally oralternatively, records of past seizures 246 may be used for diagnosingand treating the seizure. In an embodiment, all detection results may berecorded on the hard disk of a PC or on an external memory card (SD,Compact Flash, Memstick etc). In some instances, knowledge of whether aseizure occurred may be important to know the effectiveness of amedication or for other medical reasons. However, some patients areunaware of having experienced a seizure. By storing records of pastseizures 246, patient 108 may nonetheless be informed that a seizure wasexperienced. The data may include images, videos, accelerometer, orother motion sensor data. The data may include plots, summaries and/orother forms of data. The data may be analyzed and reviewed later by amedical professional for diagnosis and/or other medical purposes. Devicedrivers 248 include software for interfacing and/or controlling themotion detector.

Motion Detector with Processor (FIG. 3A)

FIG. 3A shows a block diagram of an embodiment of motion detector 110.Motion detector 110 may include output system 332, input system 334,transmitter/receiver 336, processor system 338, communications system339, memory system 340, which may include seizure detection algorithm342, characteristic seizure data 344, records of past seizures 345,device drivers 346, and/or location determining software 348. Motiondetector 110 may also include location determining hardware 350, motiondetection hardware 352 and clock 354. In other embodiments, motiondetector 110 may include additional components and/or may not includeall of the components listed above.

Output system 332 is optional and may include a display for providingfeedback regarding whether various settings are set and/or may providethe values of the current settings. Input system 334 is optional and mayinclude buttons and/or a pad for entering user settings. Optionally,output system 332 and input system 334 may include an interface forcommunications line 104 to seizure alert system 112.Receiver/transmitter 336 may include an antenna, other hardware, and/orsoftware for communicating wirelessly with other devices, such asseizure alert system 112 (e.g., via receiver 114).

Processor system 338 may be any one of, some of, any combination of, orall of multiple parallel processors, a single processor, a system ofprocessors having one or more central processors and/or one or morespecialized processors dedicated to specific tasks. Processor system 338may run a program stored on memory system for detecting seizures, whichmay be referred to as a seizure detection engine, and/or may performother functions. Communications line 339 may be a bus that allows thevarious components of motion detector 110 to communicate with oneanother.

Memory system 340 may include programs for running motion detector 110and for interfacing with other equipment. Seizure detection algorithm342, characteristic seizure data 344, records of past seizures 345,device drivers 346 have essentially the same description as seizuredetection algorithm 242, characteristic seizure data 244, records onpast seizures 246, and device drivers 248 (FIG. 2B), respectively.Location determining software 348 is optional and includes software fordetermining the location of the patient 108 for situations in whichpatient 108 is having a seizure in an otherwise unknown location. Forexample, location determining software 348 and location determininghardware 350 may be global positioning software and hardware (for a GPSsystem), respectively. Location determining software 348 and locationdetermining hardware 350 are optional and if location determiningsoftware 348 and location determining hardware 350 are globalpositioning software and hardware, location determining hardware 350 mayprocess signals from, and/or communicating with, location determiningsatellites to produce the location determining data that is furtherprocessed by location determining software 348. Motion detectionhardware 352 is the hardware that detects the motion of patient 108(FIG. 1A). Motion detection hardware 352 may include an accelerometer,which may include a cantilever with a weight attached to one end and acircuit for detecting deflections of the cantilever.

In an embodiment, the seizure detector may be included within a watch,hand strap, leg strap, and/or strapped to another part of the body. Forexample, an accelerometer/gyro sensor coupled with Bluetooth/Zigbeewireless (or USB or LAN) connectivity may be included in the watch, handstrap, and/or leg strap for detecting seizures. One or more processorsmay be attached to an arm coupled to the accelerometer or other motionsensor and/or incorporated within or attached to a mobile phone. In anembodiment, watch and the mobile phone in combination may provide acomplete system that records and analyzes data related to possibleseizures. Based on the analysis, there may be a detection of a conditionthat is expected to be a seizure, and an alert and/or other outputcommunication may be sent.

Hand-Worn “Seizure Detection” Watch

In an embodiment, seizure detection system 100 or motion detector 110may be a seizure detection watch, worn by a patient, for example. Theseizure detection watch may contain a wireless transmitter (usingBluetooth/Zigbee/Wifi/RF), an accelerometer or other motion sensor, abattery, and a processor. The sensor detects motion and generatessignals that correspond to the motion. The processor processes thesignals using a detection algorithm that analyzes the signals anddetermines (and thereby detects) whether a seizure occurred. Asdescribed in conjunction with FIG. 1A, the Bluetooth transmitter sendsthe seizure detection to the outside world via the Bluetooth, SMS, MMS,WAP, or email, IM, IP messages to another device (which may be a mobilephone, PC, Laptop, or PDA). In an embodiment, the “smart watch” is anintelligent “seizure detector” device that may do some/all the detectionand alerting. In addition the Watch, can also have some LED lightsand/or buzzer to indicate the status of detection and what the systemthinks and decides. Hence patients can look at the LEDs or hear thesound and understand the status. There may or may not be any visualdisplays, besides these lights and sounds.

Camera with Processor (FIG. 3 b)

FIG. 3B shows a block diagram of camera 360. Camera 360 may includeoutput system 362, input system 364, transmitter/receiver 366, processorsystem 368, communications system 369, memory 380, which may includeseizure detection algorithm 382, characteristic seizure data 384, anddevice drivers 386. Camera 360 may also include image detector 388 andimaging system 390. In other embodiments, camera 360 may includeadditional components and/or may not include all of the componentslisted above.

Output system 362, input system 364, transmitter/receiver 366, processorsystem 368, communications system 369, memory 380, seizure detectionalgorithm 382, characteristic seizure data 384, records on past seizures385, and device drivers 386 have similar descriptions to output system332, input system 334, transmitter/receiver 336, processor system 338,communications system 339, memory system 340, seizure detectionalgorithm 342, characteristic seizure data 344, records on past seizures345, and device drivers 346 of FIG. 3A, respectively. Also, seizuredetection algorithm 382, characteristic seizure data 384, records ofpast seizures 385, device drivers 386 have a similar description asseizure detection algorithm 242, characteristic seizure data 244,records on past seizures 246, and device drivers 248 (FIG. 2B),respectively. However, seizure detection algorithm 382 (FIG. 3B) may betailored for handling optical data, and characteristic seizure data 384and records of past seizures 385 (FIG. 3B) may be optical data, whereasseizure detection algorithm 342 (FIG. 3A) may be tailored foraccelerometer data and characteristic seizure data 344 and records ofpast seizures 345 (FIG. 3A) may be accelerometer data. Also devicedrivers 386 (FIG. 3B) may include device drivers for image detector 388and/or imaging system 390, whereas device drivers 346 (FIG. 3A) mayinclude device drivers for motion detection hardware 352.

Image detector 388 converts optical images into electrical signals thatrepresent the image and/or motion. For example, image detector 388 maybe a charge couple device. Imaging system 390 is the system of lensesand/or other optical components that form the image on image detector388.

Video Seizure Detection Algorithms

The video detection algorithm is one component of the overall system.Video detection can use one or more of at least 3 types of algorithms:

1: Optical Flow based or feature points based

2: Intelligent Motion based and/or Abnormal behavior based motionanalysis

3: Motion vectors (which may be used similar to compression methods)

Optical Flow Based Detection (FIG. 4)

FIG. 4 is a flowchart of an embodiment of a method 400 of detecting aseizure, based on optical flow. In step 402, feature points that can betracked are determined. optical flow is detected to track the path of acollection of points. Feature points can also be unique or distinguishedpoints on the person body or cloths. Feature points that can be trackedmay be corner points and points which exhibit texture in its localneighborhood, for example. The optical flow technique works by firstextracting feature points from the frame that can be tracked reliably.Optical Flow Analysis (or) Feature Point Tracking can be different.

In step 404, the optical flow analysis or feature point trackingalgorithm then tracks the motion or flow of points across successiveframes. The tracking of points may be done in each successive frame oralternatively, some frames can be skipped, depending upon the nature ofmotion.

In step 406, the paths of the tracked points are analyzed and/orplotted. For example, the plot of the points the position as a functionof time, the velocity, and/or acceleration may be extracted. Informationis computed or determined that relate to oscillations of a change ofparameter of motion, such as the frequency of the change, the amplitudeof change, time over which the change occurs (e.g., the period ofoscillation), the area swept out by the body part during the oscillatorymovements, and/or path traced by the points being tracked. The frequencyof, amplitude of, and area swept by the identified points oscillateindicate the frequency of, amplitude of, and area swept out by theoscillation, respectively. The amplitude may indicate the distance thatthe points and/or corresponding objects (e.g., body parts) are moving.Area may indicate the area swept out by a moving object. The path mayindicate the exact nature and movement patterns of the points and/orcorresponding objects. The oscillation analysis as above determineswhether a motion is classified that of a seizure. Time over which aparticular motion occurs may also be indicative of a seizure. There maybe a minimum length of time for the seizure and if the motion does notcontinue over a length of time longer than the minimum length the motionmay be classified as not being part of a seizure.

In step 408 a determination is made based on the oscillation informationwhether a seizure is occurring. Some or all of the above parametersand/or additional parameters may be used to decide if the movement is aseizure. For example, there may be one or more threshold levels for thefrequency, amplitude, and area, and if the motion crosses one or more ofthe thresholds, it may be determined that there is a seizure. Somethresholds may be proportional ratios or functions of these parameters.In step 410, if it is determined that a seizure is occurring, an alertis activated. In an embodiment, Method 400 is repeated for each set ofdata until motion detector 110 and/or the processor are turned off.

In an embodiment, each of the steps of method 400 is a distinct step. Inanother embodiment, although depicted as distinct steps in FIG. 4, step402-410 may not be distinct steps. In other embodiments, method 400 maynot have all of the above steps and/or may have other steps in additionto or instead of those listed above. The steps of method 400 may beperformed in another order. Subsets of the steps listed above as part ofmethod 400 may be used to form their own method.

Intelligent Motion Based and Abnormal Behavior Based a Motion AnalysisAlgorithm (FIG. 5)

FIG. 5 is a flowchart of an embodiment of a method 500 of detecting aseizure, based on motion analysis. In step 502, pairs of a series ofimages are taken (e.g., via a video). In step 504, pairs of consecutiveimages are compared. A video algorithm may analyze a comparison of twoimages to determine whether there is motion, which may be detected basedon pixel changes and image differencing.

In step 506, a determination is made as to whether a seizure hasoccurred based on the length and duration of the body motion.Optionally, information that is not expected to be relevant todetermining whether there was seizure is eliminated using standardtechniques. For example, information about lighting and shadows may beeliminated. In the abnormal motion case, the motion signature, duration,length, and/or area are all used to see the abnormal motion behavior.Seizure patterns can are learned and compared.

In step 508, if it is determined that a seizure is occurring, an alertis activated. Returning to step 506, if it is determined that no seizureoccurs, method 500 terminates. After termination method 500 may restarton another set of data. In an embodiment, many instances of method 500may be performed concurrently on different set of data. For example,after a first instance of method 500 starts working on one pair ofimages, a second instance may start working on the next set of data,which may come from the next available pair of images, before the firstinstance of method 500 terminates. In an embodiment, method 500 isrepeated for each set of data until motion detector 110 and/or theprocessor are turned off.

In an embodiment, each of the steps of method 500 is a distinct step. Inanother embodiment, although depicted as distinct steps in FIG. 5, step502-508 may not be distinct steps. In other embodiments, method 500 maynot have all of the above steps and/or may have other steps in additionto or instead of those listed above. The steps of method 500 may beperformed in another order. Subsets of the steps listed above as part ofmethod 500 may be used to form their own method.

Motion Vectors Based Algorithm (FIG. 6)

FIG. 6 is a flowchart of an embodiment of a method 600 of detecting aseizure, based on patterns of motion vector patterns. In step 602, aseries of images is taken. In step 604, motion vectors are computed.Specifically, two-dimensional vectors that provide offsets from thecoordinates in one picture frame to the coordinates in another pictureframe are computed. The vectors may be created in a manner similar tothe motion vectors created for compression methods or same motionvectors from the compressed video may be used. In an embodiment, themotion vectors are created using IP cameras. In step 606, movementpattern signatures are determined from the motion vectors. In step 608,the movement pattern signatures measured are compared to movementpattern signatures of seizures. A signature may be used for comparisonwith pattern signatures that are determined to fit a signature thatresults from a seizure, and if the signature of the pattern measured isclose enough to (e.g., within a threshold value of the root mean squareof the differences between) the signature of the seizure, an indicationthat a seizure occurred is generated. In step 610, if it is determinedthat a seizure is occurring, an alert it activated.

Returning to step 608, if it is determined that no seizure occurs,method 600 terminates. After termination method 600 may restart onanother set of data. In an embodiment, many instances of method 600 maybe performed concurrently on different set of data. For example, after afirst instance of method 600 starts working on one pair of images, asecond instance may start working on the next set of data, which maycome from the next available pair of images, before the first instanceof method 600 terminates. In an embodiment, method 600 is repeated foreach set of data until motion detector 110 and/or the processor areturned off. Finally, the algorithm may also be a hybrid algorithm of all3 video detection methods; or, it may also be a combination/hybrid ofVideo and Motion/Accelerometer/Sensor algorithms joined and fusedtogether.

In an embodiment, each of the steps of method 600 is a distinct step. Inanother embodiment, although depicted as distinct steps in FIG. 6, step602-610 may not be distinct steps. In other embodiments, method 600 maynot have all of the above steps and/or may have other steps in additionto or instead of those listed above. The steps of method 600 may beperformed in another order. Subsets of the steps listed above as part ofmethod 600 may be used to form their own method.

Accelerometer Seizure Detection Algorithm (FIG. 7)

FIG. 7 is a flowchart of an embodiment of a method 700 of detectingseizures by measuring motion, via an accelerometer (and/or gyro sensor).An accelerometer sensor provides the acceleration and orientation of thebody. The accelerometer is secured to the patient's hand, arm, legs,and/or any other part of body that shakes and has the seizure movements.The following steps are used to detect the seizure from theaccelerometer data.

In step 702, the sensor data is obtained by a time based sampling of themotion. For example, 1000 or 10,000 samples per second are collected. Inan embodiment, the samples may be an amount of deflection of acantilever. The data sampling may be in any one or all X, Y, and Z axes.The data per axis may be one dimensional numerical accelerometer data.The motion data may be sampled over time. Hence for the X and Y axes,there will be two data streams. For triple axes data from axes X, Y, andZ, there will be 3 data streams. In an embodiment, the amplitude andfrequency are measured as part of the time based data stream. Theamplitude, frequency, change of position, and acceleration may bemeasured from the accelerometer or other motion sensor data. The datamay be tracked over time. The data can be tracked in every time intervalor specified time internals may be skipped. The time intervals at whichthe data is sampled define the sampling frequency.

In optional step 704, the path of the data is analyzed or plotted. Datasuch as points, position, acceleration, velocity, and/or speed areextracted. If the points, positions, velocities, and acceleration werealready determined as part of step 702, step 704 may be skipped.

In step 706, an oscillation analysis is performed, as described above,may be used to determine whether a seizure takes place. The frequency,amplitude, time, and/or path of the oscillation may be determined.Frequency may determine how frequently the sensor and objects oscillate.The amplitude may indicate the amount of distance that the objects move.The path may indicate the exact nature and movement patterns of theobjects. The frequency, amplitude, and/or path may be used to analyzeoscillations and decide if the movement is a seizure.

Returning to step 708, if it is determined that no seizure occurs,method 700 terminates. After termination method 700 may restart onanother set of data. In an embodiment, many instances of method 700 maybe performed concurrently on different set of data. For example, after afirst instance of method 700 starts working on one pair of images, asecond instance may start working on the next set of data, which maycome from the next available pair of images, before the first instanceof method 700 terminates. In an embodiment, method 700 is repeated foreach set of data until motion detector 110 and/or the processor areturned off.

The detection algorithm can use any of one or more of the followingmathematical methods. In one embodiment, the peak and amplitude of theoscillation are checked, and compared to thresholds value of the peaksand amplitude. In an embodiment, if the peak and/or amplitude aregreater than the threshold, then a determination is made that theoscillation is associated with a seizure. An absolute and/or relativethreshold may be used to find abnormalities, which may indicate aseizure.

In another embodiment, a search is made for repeated peaks and valleysin the one dimensional sensor data (for X, Y, and/or Z). This technologyuses the motion vector patterns based algorithm. Repeated anddistinguished peaks and valleys may indicate seizures.

In another embodiment, a search is made for duplicate peaks on otheraxes. In other words, one axis may have stronger peaks while the othersmay have weaker peaks. In some cases all axes can be stronger or weaker.However, neighboring axes can provide a valuable confirmation when thepeaks on one axis have corresponding peaks on another axis.

In another embodiment, software and/or hardware neural networks or otherlearning methods may determine abnormal patterns to detect seizures. Inanother embodiment, exact template patterns or signal patterns can beused to match other signal patterns. A prior known seizure pattern canbe used to compare the signal pattern with the known template and if thepattern detected matches the known seizure pattern within a giventolerance, then it is expected that a seizure occurred. These neuralnetworks or other machines using other learning methods may be eithersupervised or unsupervised learning.

In another embodiment, the position may be analyzed by determining thefirst derivative and the second derivative of a signal that isindicative of the position as a function of time. The first and secondderivative of the position signal may be monitored to determine whetherthe first and second derivative are within a range that is considered tobe an average and/or normal change of position (an average and normalfirst derivative dx/dt and an average and normal rate of change ofposition, which is the 2^(nd) derivative, d²x/dt²). When the firstand/or second derivatives are abnormal, or beyond a threshold then anindication is generated that a seizure may have been detected.Additionally, if the periodic changes of the first and second derivativeare outside of a certain range, it may be an indication that a seizurehas occurred. Periodic changes in the second derivate are the thirdderivates, which are the impulses, which may be used to characterizejerky motion. Similarly, if the third derivative (or another derivative)is beyond a threshold or is not within a range that is considerednormal, an indication that a seizure occurred may be generated.Additionally, if the pattern of times at which the third derivativesrise above a certain threshold matches that of a known pattern for aseizure and/or occur at a frequency that is expected to be indicative ofa seizure, an indication that a seizure has occurred in generated.

In another embodiment, statistical learning or probabilistic methods areused. Machine learning strategies based on Bayesian Network (Bayes net)and HMM (Hidden Markov Models) and other statistical learning orprobabilistic methods can be used for detection of seizure.

In another embodiment, local, regional, and/or global features aredetected. The features may be a collection of data taken from aneighborhood of a signal with temporal information. Local features arecharacteristics of signal and/or data in a small region of the data.Local features are a function of time (e.g., the features of a plot ofthe signal as a function of time), regional features covers more time,and global features are an average or a collection of local/regionalfeatures over longer time. Both local and global features can be acombination of both shape and time/temporal based. The local and globalfeatures are detected and compared to known local, regional and globalfeatures that are expected to characterize seizure features to determinewhether a seizure has occurred. If the local, regional and globalfeatures that are associated with a seizure occur, then a signal isgenerated indicating that seizure has occurred.

Individual Person's Seizure Signature

In one mode or embodiment, a person's seizure data or motion signaturemay be measured as a seizure occurs. “Seizure detection” parameters(frequency, amplitude, patterns) can be customized as a “seizuresignature” for each patient. This signature can be adjusted andconfigured for each patient if required for higher accuracy, as opposedto standard factory defaults.

Instead of a fixed seizure signature, the person's signature may bedetermined over time. Then the detection algorithm will adapt andfine-tune the seizure detection parameters based on the individuals'signature patterns—such as frequency, amplitude, time, area, path,and/or other parameters).

FIG. 8 shows a graph 800 of three orthogonal components of accelerationof an arm. Graph

Graph 800 includes a vertical axis 802 a-c, horizontal axis 804 a-c, andplots 806, 808, and 810. Horizontal axis 802 a-c is the time axis, andvertical axes 804 a-c are the amplitude axes. Plots 806, 808, and 810are plots of each of the three components of acceleration labeled X, Y,and Z, which are measured in a reference frame that is stationary withrespect to the wrist. Graph 800 shows 9 peaks with about 5 second. Thethreshold for the number of peaks within a window of 6 seconds should be9 peaks or less. The magnitude for the peaks of the y component ofacceleration is 6 cm/sec², and the threshold for a single component ofacceleration should be less than 6 cm/sec².

FIG. 9 shows a graph 900 of two parameters of motion. Graph 900 includesa vertical axis 902, horizontal axis 904, and plots 906, 908, and 910.Horizontal axis 902 is the time axis, and vertical axes 904 a-c is theamplitude axis. Plot 906 plots the magnitude of the acceleration vector.Plot 908 plots the first derivative of the magnitude of acceleration.Plot 910 is also a plot of the are plots of the first derivative of themagnitude of acceleration. However, peaks that were below apredetermined threshold were removed. If the number of peaks within aspecified window of time are greater than predetermined number, it is anindication of a seizure.

In FIGS. 8 and 9 the units for acceleration are m/sec² and the units forimpulse or jerk are m/sec³. In an embodiment, for at least some patientsthe threshold for the magnitude of acceleration may be about 7 m/sec².The threshold for a “jerk” or impulse, may be about a number less than 4m/sec³ e.g., 3 m/sec³, 3.5 m/sec³, or 3.8 m/sec³. The threshold for thefrequency of peaks in acceleration may 3/(3 seconds) (e.g., 1 Hz). Thenumber peaks in a window of 3 seconds there should be at least 3 peaks.The threshold for the number of peaks in the impulse should be at least3, and within a window of about 3 seconds there should be at least 3peaks in the impulse/jerk. In other embodiments, the units and thevalues for the thresholds above may be proportional to those givenabove.

FIG. 10 shows an embodiment of a seizure detection, analyzing, andmonitoring device.

Extensions and Alternatives

Each embodiment disclosed herein may be used or otherwise combined withany of the other embodiments disclosed. Any element of any embodimentmay be used in any embodiment.

Although the invention has been described with reference to specificembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the true spirit and scope of theinvention. In addition, modifications may be made without departing fromthe essential teachings of the invention.

1. A method comprising: collecting data, at a machine having a processorsystem including at least one processor and a memory unit, related tomotions associated with a person; analyzing, by the processor system,the data collected to determine one or more values characterizing themotion; and comparing, by the machine, the one or more valuescharacterizing the motion to one or more values characterizing motion ofa medically abnormal condition; determining, by the machine, whether theone or more values characterizing the motion match the one or morevalues characterizing a medically abnormal condition based on thecomparing; and activating, by the machine, an alert if as a result ofthe determining it is determined that the one or more valuescharacterizing the motion matches the one or more values characterizingthe medically abnormal condition.
 2. The method of claim 1, the one ormore values characterizing the motion of a medically abnormal conditionincluding at least a threshold value for a frequency of oscillation of abody part; the comparing including at least comparing the one or morevalues characterizing the motion, collected during the collecting, tothe threshold; and the determining including at least determiningwhether the value crossed the threshold based on the comparing.
 3. Themethod of claim 1, the one or more values characterizing motion of themedically abnormal condition including one or more motion patternscharacterizing a medically abnormal condition; the comparing includingat least comparing a motion pattern derived from the data collectedduring the collecting to the one or more motion patterns characterizingthe medically abnormal condition; and the determining including at leastdetermining whether the motion pattern derived matches within apredetermined tolerance one of the one or more motion patternscharacterizing the medically abnormal condition.
 4. The method of claim1, further comprising storing the data in long term memory for lateranalysis.
 5. The method of claim 1, the analyzing including at leastidentifying distinguishable points or features, and determininglocations of the distinguishable points or features across multiplepicture frames to determine a path for each of a set of thedistinguishable points or features; the comparing including at leastcomparing the path for each of the set of the distinguishable points orfeatures to paths characterizing the medically abnormal condition; andthe determining including at least determining whether the path for eachof the set of the distinguishable points or features matches pathscharacterizing the medically abnormal condition within a predeterminedtolerance.
 6. The method of claim 1, the analyzing including at leastdetermining oscillatory motion; and determining one or more parameterscharacterizing the oscillatory motion.
 7. The method of claim 6, the oneor more parameters including at least a frequency of oscillation; thecomparing including at least comparing the frequency of oscillation to apredetermined threshold; and the determining including at leastdetermining whether the frequency of oscillation is higher than thepredetermined threshold the activating of the alert including activatingthe alert if the frequency of oscillation is higher than thepredetermined threshold.
 8. The method of claim 7, the one or moreparameters being output of an accelerometer.
 9. The method of claim 7,the one or more parameters being output of a gyro sensor.
 10. The methodof claim 7, the one or more parameters being output a a combination ofone or more accelerometers and gyro sensors.
 11. The method of claim 7,the one or more parameters being derived from an optical flow or featurepoint analysis.
 12. The method of claim 7, the one or more parametersbeing derived from motion vectors.
 13. The method of claim 1, theactivating of the alert including at least sending a message to a deviceassociated with a concerned party.
 14. The method of claim 13, themessage including data from a current episode of abnormal motion. 15.The method of claim 13, the message including a current location of theperson.
 16. The method of claim 1, the analyzing including at leastidentifying distinguishable points or features, and determininglocations of the distinguishable points or features across multiple-datasampling to determine a path for each of a set of the distinguishablepoints or features; the determining whether the motion data indicatesthat the medically abonormal motion has occurred including at leastcomparing the path for each of the set of the distinguishable points orfeatures to paths characterizing the medically abnormal motion; and thedetermining of locations including at least determining whether the pathfor each of the set of the distinguishable points or features matchespaths characterizing a the medically abnormal motion within apredetermined tolerance.
 17. The method of claim 1, the medicallyabnormal motion being a seizure
 18. The system of claim 1, the analyzingincluding at least identifying distinguishable points or features, anddetermining locations of the distinguishable points or features acrossmultiple-data sampling to determine a path for each of a set of thedistinguishable points or features; the determining whether the motiondata indicates that a specific type of motion has occurred including atleast comparing the path for each of the set of the distinguishablepoints or features to paths characterizing a specific type of motion;and the determining of locations including at least determining whetherthe path for each of the set of the distinguishable points or featuresmatches paths characterizing a specific type of motion within apredetermined tolerance.
 19. The system of claim 18, the algorithm alsoincluding windowing the data being collected; the analyzing including atleast determining a difference between a minimum accelerations andmaximum accelerations during a window of time; the determining whetherthe motion data indicates that a specific type of motion has occurredincluding at least determining whether the difference is greater than athreshold value that is indicative of a specific type of motion.
 20. Thesystem of claim 1, the analyzing including at least determining how manyjerks occur during a period of time; the determining of whether themotion indicates a specific type of motion includes at least determiningwhether the number of jerks is greater than a threshold.
 21. The systemof claim 20, each jerk of the jerks being an absolute value of anumerical estimate of a first derivative of a magnitude of acceleration.22. The system of claim 1, the analyzing including at least computing asecond derivative of acceleration of the one or more parts of the bodyduring a specified window; the determining of whether the motionindicates a specific type of motion includes at least determining howmany second derivative within the specified window have crossed athreshold indicative of a specific type of motion.
 23. A systemcomprising: a body-worn portable device including at least a strap forstrapping the portable device onto a person; an input system forinputting seizure detections parameters; an accelerometer or gyro sensorfor measuring motion data; a housing for enclosing the accelerometer orgyro sensor, the display being attached to the housing for displayingsetting for a specific type of motion and status information, and theinput system being attached to the housing in a manner in which thesetting for the specific type of motion may be entered by the person;and the remote unit, which is a unit remote from the body-worn portabledevice, including at least a receiver for receiving motion data from thewrist-worn portable device; a memory having stored thereoncharacteristics of a specific type of motion, and an algorithm foranalyzing the motion data measured, comparing tile seizure thecharacteristics of the specific type of motion to the motion datameasured, and determining whether to send an alert based on thecomparing; and a processor that implements the algorithm and generatesan indication that abnormal motion has occurred based on the algorithm.